Tag Archives: distributive shock

For this week’s discussion, I have chosen to analyze an article (Sakr et al., 2006) that attempts to outline the efficacy and potential dangers of certain drugs used to treat shock. As a critical care paramedic, the discussion surrounding this article can provide insight to choosing alternative therapies when caring for my patients, but it is important for me to understand the potential biases and limitations of such a study that could lead to flawed conclusions (Gluud, 2006).

Sakr et al. (2006) collected data on ICU admissions over a two week period to further understand how dopamine effects mortality and morbidity when administered in response to hemodynamic compromise. Also, other administered vasoactive drugs were included in the analysis whether administered concomitantly with dopamine or instead of dopamine. The researchers did not distinguish between etiologies except to delineate between septic shock and non-septic shock. Patients who presented with shock or suffered a shock state within the first 24 hours of admission were included in the analysis. Patients admitted to the ICU mainly for 24 hour surgical observation where not included.

Shock is defined as “a state of inadequate cellular sustenance associated with inadequate or inappropriate tissue perfusion resulting in abnormal cellular metabolism” (Hillman & Bishop, 2004, p. 121). There are many etiologies of shock, including sepsis, anaphylactic, neurogenic, hypovolemic, cardiogenic, and others, which respond differently to various therapies. This confounder creates an information bias, as this variable is not identified in the data collection and cannot be scrutinized. Simply identifying the etiology of each shock state would limit this bias. The researchers, however, acknowledge this limitation and others.

Another confounding variables is the time constraint of the data. In regards to septic shock, this variable becomes evident. Many pathogens spread predictively during certain times of the year. The concomitant treatment of these infections could predispose patients to suffer a prolonged state of shock (in cases where the pathogen might not be immediately recognized) or provide for an ideal treatment pathway when the pathogen and the antibiotic regimen are fully understood and effective. This selection bias could be controlled by choosing patients who present throughout the year.

As Gluud (2006) points out:

When intervention effects are moderate or small, the human processing of data, unsystematic data collection, and the human capacity to overcome illnesses spontaneously limit the value of uncontrolled observations. Experimental models are essential for estimation of toxicity and pathophysiology.(p. 494)